UC Riverside

This thesis explores three projects, offering valuable insights into the dynamic processes governing biological systems. The first project focuses on Cowpea chlorotic mottle virus (CCMV) as a model for virus assembly and disassembly studies. A novel model based on classical nucleation theory explains spontaneous and reversible size conversion of empty CCMV capsids by accounting for the change in free protein concentration during capsid assembly and disassembly.

The second project focuses on growth regulation mechanisms in the \textit{Drosophila} wing disc tissue, an ideal model for studying developmental processes. We develop a multiscale chemical-mechanical model that integrates morphogen gradients, mechanical forces, and tissue dynamics. By comparing spatial distribution of dividing cells in simulations with experimental data, we reveal the critical role of the Dpp morphogen gradient in determining tissue size and shape. If the Dpp gradient spreads in a larger domain a larger tissue size with more symmetric shape can be achieved at a faster growth rate. Additionally, feedback regulation involving Dpp receptor downregulation enables further morphogen spreading, prolonging tissue growth at a spatially homogeneous rate. This comprehensive model provides a deeper understanding of the interplay between chemical signals and mechanical forces, illuminating the mechanisms controlling tissue growth.

The third project concentrates on bacterial behavior, utilizing a sub-cellular element model to understand dynamics of bacterial chemotactic behavior and its impact on bacterial trajectories. We have investigated bacterial swimming patterns including run-reverse and run-wrap reverse, in addition to chemotaxis strategies in which the bacteria exhibit different responses to the chemoattractant in different swimming modes, including cases where they may not react at all. We have found that simpler motility patterns emerge greater chemotaxis efficiency compared to complicated swimming patterns. In addition, we have discovered that a complicated swimming pattern could lead to bacterial aggregation, only if the dominant swimming mode is involved in the chemotaxis strategy. Based on our simulations, it has been found that bacteria with a simple swimming pattern lacking in enough reorientations can recover their chemotactic behavior by adopting a more complex pattern even if the bacteria won't respond to the chemical gradient in the adopted mode.